Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1785
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Master Term Project Prediction of Credit Card Default(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Akalın, Selçuk; Utku KoçAs profitable customer acquisition becomes more and more critical for the banking sector in terms of competition, the requirement to predict customer defaults with different machine learning algorithms is increasing. Thanks to similar practices, possible damages can be prevented. Due to the rapid change of machine learning with the changing technology, the fields of application and development in different sectors are also changing and developing rapidly. In this study, the aim is to make a comparison over model outcomes and making observations on outcomes to determine the areas that can be developed or researched with running different supervised and unsupervised machine learning algorithms on the final dataset gathered by doing following methods such as key points discovered in exploratory data analysis on an imbalanced credit card dataset, generating different features according to learned key points, eliminating imbalance with different oversampling and undersampling methods.Master Term Project Forecasting Organic Traffic With Different Source of Data(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Çolak, Mehtap; Özgür ÖzlükIn this project, the results are compared using different data sets for the organic traffic forecasting of a website. Two different models were developed based on the data obtained from Google Search Console (GSC), Google Analytics (GA), Ahrefs and Google Trends and trained with XGBoost and Random Forest machine learning algorithms. Although the .. value and accuracy rate of the first model developed on the GSC, GA and Ahrefs data obtained between 2019-2020 was high; it is not suitable for predictive analysis because the data sets consist of dependent variables. The second model was developed with Google Trends data for brand and non-brand queries with the highest Impression value. The future trends of the relevant queries were predicted using the Prophet algorithm. Through this model, Impression values of the relevant website were estimated for the remainder of 2021.Master Term Project